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Quantification of Asphalt Mixture Interlocking Utilizing 2D and 3D Image Processing

delete2023-01-01
delete11
PRE
AI
P
Pawel Polaczyk
Y
Yuetan Ma
Z
Zaher A. Jarrar
X
Xi Jiang
R
Rui Xiao
Baoshan Huang 封面图
Baoshan Huang (Baoshan Huang) *
DOI:10.1061/(ASCE)MT.1943-5533.0004560delete
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摘要

摘要

En 中文
The internal structure of the asphalt mixture plays a vital role in performance because it addresses gradation, air void distribution, and mixture packing. Although the quantification of the effect of the internal structure on the mechanical behavior of hot mix asphalt cannot be achieved through experimental work, the internal structure can be characterized through image-based analysis. Recent studies used imaging techniques to understand how the internal structure influences the performance of a mixture. This study attempted to quantify the interlocking properties of hot mix asphalt using three-dimensional (3D) and two-dimensional (2D) image analyzing processes and correlate the result to the locking point concept. Two types of aggregates (limestone and gravel), one type of bitumen PG64-22, and two types of asphalt mixtures-surface and base (binder)-were utilized in this study. Two software packages were used to process and analyze the captured images: iPas2 for the 2D analysis and Avizo Fire version 9.7 for the 3D analysis. Three parameters were utilized to quantify the interlocking properties, such as the number of contacts between aggregates per volume and area, ratio of interlocked particles to total number of particles, and contact area and length. This study's results show that 3D and 2D image processing represent promising nondestructive methods to quantify asphalt mixtures' interlocking properties. The parameters evaluated in this research indicated that the locking point definitions LP3/2-2-3 are the most appropriate to quantify the interlocking of the asphalt mixture.
Keyword:
Compaction
Asphalt mix
Locking point
Image processing
X-ray

期刊

J
Journal of Materials in Civil Engineering
IF:
3
论文数:
7.2K
被引数:
2.4W

机构

University of Tennessee System 封面图
University of Tennessee System
学者数:
2.9W
论文数: 2.7W
被引数: 115
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